library("FRESA.CAD")
library(readxl)
library(igraph)
library(umap)
library(tsne)
library(entropy)
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)
Data from the speech features
TADPOLE_D1_D2 <- read.csv("~/GitHub/BSWiMS/Data/TADPOLE/TADPOLE_D1_D2.csv")
TADPOLE_D1_D2_Dict <- read.csv("~/GitHub/BSWiMS/Data/TADPOLE/TADPOLE_D1_D2_Dict.csv")
TADPOLE_D1_D2_Dict_LR <- as.data.frame(read_excel("~/GitHub/BSWiMS/Data/TADPOLE/TADPOLE_D1_D2_Dict_LR.xlsx",sheet = "LeftRightFeatures"))
rownames(TADPOLE_D1_D2_Dict) <- TADPOLE_D1_D2_Dict$FLDNAME
# mm3 to mm
isVolume <- c("Ventricles","Hippocampus","WholeBrain","Entorhinal","Fusiform","MidTemp","ICV",
TADPOLE_D1_D2_Dict$FLDNAME[str_detect(TADPOLE_D1_D2_Dict$TEXT,"Volume")]
)
#TADPOLE_D1_D2[,isVolume] <- apply(TADPOLE_D1_D2[,isVolume],2,'^',(1/3))
TADPOLE_D1_D2[,isVolume] <- TADPOLE_D1_D2[,isVolume]^(1/3)
# mm2 to mm
isArea <- TADPOLE_D1_D2_Dict$FLDNAME[str_detect(TADPOLE_D1_D2_Dict$TEXT,"Area")]
TADPOLE_D1_D2[,isArea] <- sqrt(TADPOLE_D1_D2[,isArea])
# Get only cross sectional measurements
FreeSurfersetCross <- str_detect(colnames(TADPOLE_D1_D2),"UCSFFSX")
# The subset of baseline measurements
baselineTadpole <- subset(TADPOLE_D1_D2,VISCODE=="bl")
table(baselineTadpole$DX)
Dementia Dementia to MCI MCI MCI to Dementia
7 336 1 864 5
MCI to NL NL NL to MCI
2 521 1
table(baselineTadpole$DX_bl)
AD CN EMCI LMCI SMC 342 417 310 562 106
rownames(baselineTadpole) <- baselineTadpole$PTID
validBaselineTadpole <- cbind(DX=baselineTadpole$DX_bl,
AGE=baselineTadpole$AGE,
Gender=1*(baselineTadpole$PTGENDER=="Female"),
ADAS11=baselineTadpole$ADAS11,
ADAS13=baselineTadpole$ADAS13,
MMSE=baselineTadpole$MMSE,
RAVLT_immediate=baselineTadpole$RAVLT_immediate,
RAVLT_learning=baselineTadpole$RAVLT_learning,
RAVLT_forgetting=baselineTadpole$RAVLT_forgetting,
RAVLT_perc_forgetting=baselineTadpole$RAVLT_perc_forgetting,
FAQ=baselineTadpole$FAQ,
Ventricles=baselineTadpole$Ventricles,
Hippocampus=baselineTadpole$Hippocampus,
WholeBrain=baselineTadpole$WholeBrain,
Entorhinal=baselineTadpole$Entorhinal,
Fusiform=baselineTadpole$Fusiform,
MidTemp=baselineTadpole$MidTemp,
ICV=baselineTadpole$ICV,
baselineTadpole[,FreeSurfersetCross])
LeftFields <- TADPOLE_D1_D2_Dict_LR$LFN
names(LeftFields) <- LeftFields
LeftFields <- LeftFields[LeftFields %in% colnames(validBaselineTadpole)]
RightFields <- TADPOLE_D1_D2_Dict_LR$RFN
names(RightFields) <- RightFields
RightFields <- RightFields[RightFields %in% colnames(validBaselineTadpole)]
## Normalize to ICV
validBaselineTadpole$Ventricles=validBaselineTadpole$Ventricles/validBaselineTadpole$ICV
validBaselineTadpole$Hippocampus=validBaselineTadpole$Hippocampus/validBaselineTadpole$ICV
validBaselineTadpole$WholeBrain=validBaselineTadpole$WholeBrain/validBaselineTadpole$ICV
validBaselineTadpole$Entorhinal=validBaselineTadpole$Entorhinal/validBaselineTadpole$ICV
validBaselineTadpole$Fusiform=validBaselineTadpole$Fusiform/validBaselineTadpole$ICV
validBaselineTadpole$MidTemp=validBaselineTadpole$MidTemp/validBaselineTadpole$ICV
leftData <- validBaselineTadpole[,LeftFields]/validBaselineTadpole$ICV
RightData <- validBaselineTadpole[,RightFields]/validBaselineTadpole$ICV
## get mean and relative difference
meanLeftRight <- (leftData + RightData)/2
difLeftRight <- abs(leftData - RightData)
reldifLeftRight <- difLeftRight/meanLeftRight
colnames(meanLeftRight) <- paste("M",colnames(meanLeftRight),sep="_")
colnames(difLeftRight) <- paste("D",colnames(difLeftRight),sep="_")
colnames(reldifLeftRight) <- paste("RD",colnames(reldifLeftRight),sep="_")
validBaselineTadpole <- validBaselineTadpole[,!(colnames(validBaselineTadpole) %in%
c(LeftFields,RightFields))]
#validBaselineTadpole <- cbind(validBaselineTadpole,meanLeftRight,difLeftRight,reldifLeftRight)
validBaselineTadpole <- cbind(validBaselineTadpole,meanLeftRight,difLeftRight)
## Remove columns with too many NA more than %15 of NA
nacount <- apply(is.na(validBaselineTadpole),2,sum)/nrow(validBaselineTadpole) < 0.15
diagnose <- validBaselineTadpole$DX
pander::pander(table(diagnose))
| AD | CN | EMCI | LMCI | SMC |
|---|---|---|---|---|
| 342 | 417 | 310 | 562 | 106 |
validBaselineTadpole <- validBaselineTadpole[,nacount]
## Remove character columns
ischar <- sapply(validBaselineTadpole,class) == "character"
validBaselineTadpole <- validBaselineTadpole[,!ischar]
## Place back diagnose
validBaselineTadpole$DX <- diagnose
validBaselineTadpole <- validBaselineTadpole[complete.cases(validBaselineTadpole),]
ischar <- sapply(validBaselineTadpole,class) == "character"
validBaselineTadpole[,!ischar] <- sapply(validBaselineTadpole[,!ischar],as.numeric)
colnames(validBaselineTadpole) <- str_remove_all(colnames(validBaselineTadpole),"_UCSFFSX_11_02_15_UCSFFSX51_08_01_16")
colnames(validBaselineTadpole) <- str_replace_all(colnames(validBaselineTadpole)," ","_")
validBaselineTadpole$LONISID <- NULL
validBaselineTadpole$IMAGEUID <- NULL
validBaselineTadpole$LONIUID <- NULL
diagnose <- as.character(validBaselineTadpole$DX)
validBaselineTadpole$DX <- diagnose
pander::pander(table(validBaselineTadpole$DX))
| AD | CN | EMCI | LMCI | SMC |
|---|---|---|---|---|
| 245 | 359 | 272 | 444 | 93 |
validBaselineTadpole[validBaselineTadpole$DX %in% c("EMCI","LMCI"),"DX"] <- "MCI"
validBaselineTadpole[validBaselineTadpole$DX %in% c("CN","SMC"),"DX"] <- "NL"
pander::pander(table(validBaselineTadpole$DX))
| AD | MCI | NL |
|---|---|---|
| 245 | 716 | 452 |
subjectsID <- rownames(validBaselineTadpole)
visitsID <- unique(TADPOLE_D1_D2$VISCODE)
baseDx <- TADPOLE_D1_D2[TADPOLE_D1_D2$VISCODE=="bl",c("PTID","DX","EXAMDATE")]
rownames(baseDx) <- baseDx$PTID
baseDx <- baseDx[subjectsID,]
lastDx <- baseDx
toDementia <- baseDx
table(lastDx$DX)
Dementia Dementia to MCI MCI MCI to Dementia MCI to NL
244 1 711 2 2
NL NL to MCI
452 1
hasDementia <- lastDx$PTID[str_detect(lastDx$DX,"Dementia")]
for (vid in visitsID)
{
DxValue <- TADPOLE_D1_D2[TADPOLE_D1_D2$VISCODE==vid,c("PTID","DX","EXAMDATE")]
rownames(DxValue) <- DxValue$PTID
DxValue <- DxValue[DxValue$PTID %in% subjectsID,]
noDX <- DxValue$PTID[nchar(DxValue$DX) < 1]
print(length(noDX))
DxValue[noDX,] <- lastDx[noDX,]
inLast <- lastDx$PTID[lastDx$PTID %in% DxValue$PTID]
print(length(inLast))
lastDx[inLast,] <- DxValue[inLast,]
noDementia <- !(toDementia$PTID %in% hasDementia)
toDementia[noDementia,] <- lastDx[noDementia,]
hasDementia <- unique(c(hasDementia,lastDx$PTID[str_detect(lastDx$DX,"Dementia")]))
}
[1] 0 [1] 1413 [1] 2 [1] 1326 [1] 6 [1] 1218 [1] 23 [1] 1095 [1] 805 [1] 1058 [1] 29 [1] 710 [1] 20 [1] 212 [1] 14 [1] 167 [1] 32 [1] 553 [1] 25 [1] 298 [1] 18 [1] 130 [1] 667 [1] 667 [1] 112 [1] 112 [1] 176 [1] 176 [1] 177 [1] 177 [1] 625 [1] 625 [1] 251 [1] 251 [1] 159 [1] 159 [1] 7 [1] 7 [1] 17 [1] 99 [1] 9 [1] 63 [1] 1 [1] 1
table(lastDx$DX)
Dementia Dementia to MCI MCI MCI to Dementia MCI to NL
428 2 463 80 7
NL NL to Dementia NL to MCI
406 1 26
baseMCI <-baseDx$PTID[baseDx$DX == "MCI"]
lastDementia <- lastDx$PTID[str_detect(lastDx$DX,"Dementia")]
lastDementia2 <- toDementia$PTID[str_detect(toDementia$DX,"Dementia")]
lastNL <- lastDx$PTID[str_detect(lastDx$DX,"NL")]
MCIatBaseline <- baseDx[baseMCI,]
MCIatEvent <- toDementia[baseMCI,]
MCIatLast <- lastDx[baseMCI,]
MCIconverters <- MCIatBaseline[baseMCI %in% lastDementia,]
MCI_No_converters <- MCIatBaseline[!(baseMCI %in% MCIconverters$PTID),]
MCIconverters$TimeToEvent <- (as.Date(toDementia[MCIconverters$PTID,"EXAMDATE"])
- as.Date(MCIconverters$EXAMDATE))
sum(MCIconverters$TimeToEvent ==0)
[1] 0
MCIconverters$AtEventDX <- MCIatEvent[MCIconverters$PTID,"DX"]
MCIconverters$LastDX <- MCIatLast[MCIconverters$PTID,"DX"]
MCI_No_converters$TimeToEvent <- (as.Date(lastDx[MCI_No_converters$PTID,"EXAMDATE"])
- as.Date(MCI_No_converters$EXAMDATE))
MCI_No_converters$LastDX <- MCIatLast[MCI_No_converters$PTID,"DX"]
MCI_No_converters <- subset(MCI_No_converters,TimeToEvent > 0)
MCIPrognosisIDs <- c(MCIconverters$PTID,MCI_No_converters$PTID)
TADPOLECrossMRI <- validBaselineTadpole[MCIPrognosisIDs,]
table(TADPOLECrossMRI$DX)
MCI 680
TADPOLECrossMRI$DX <- NULL
TADPOLECrossMRI$status <- 1*(rownames(TADPOLECrossMRI) %in% MCIconverters$PTID)
table(TADPOLECrossMRI$status)
0 1 436 244
studyName <- "TADPOLE"
dataframe <- TADPOLECrossMRI
outcome <- "status"
TopVariables <- 10
thro <- 0.60
cexheat = 0.15
Some libraries
library(psych)
library(whitening)
library("vioplot")
library("rpart")
pander::pander(c(rows=nrow(dataframe),col=ncol(dataframe)-1))
| rows | col |
|---|---|
| 680 | 327 |
pander::pander(table(dataframe[,outcome]))
| 0 | 1 |
|---|---|
| 436 | 244 |
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
largeSet <- length(varlist) > 1500
Scaling and removing near zero variance columns and highly co-linear(r>0.99999) columns
### Some global cleaning
sdiszero <- apply(dataframe,2,sd) > 1.0e-16
dataframe <- dataframe[,sdiszero]
varlist <- colnames(dataframe)[colnames(dataframe) != outcome]
tokeep <- c(as.character(correlated_Remove(dataframe,varlist,thr=0.99999)),outcome)
dataframe <- dataframe[,tokeep]
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
iscontinous <- sapply(apply(dataframe,2,unique),length) >= 5 ## Only variables with enough samples
dataframeScaled <- FRESAScale(dataframe,method="OrderLogit")$scaledData
numsub <- nrow(dataframe)
if (numsub > 1000) numsub <- 1000
if (!largeSet)
{
hm <- heatMaps(data=dataframeScaled[1:numsub,],
Outcome=outcome,
Scale=TRUE,
hCluster = "row",
xlab="Feature",
ylab="Sample",
srtCol=45,
srtRow=45,
cexCol=cexheat,
cexRow=cexheat
)
par(op)
}
The heat map of the data
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
#cormat <- Rfast::cora(as.matrix(dataframe[,varlist]),large=TRUE)
cormat <- cor(dataframe[,varlist],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Original Correlation",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.9996707
DEdataframe <- IDeA(dataframe,verbose=TRUE,thr=thro)
#>
#> Included: 326 , Uni p: 0.008991707 , Uncorrelated Base: 152 , Outcome-Driven Size: 0 , Base Size: 152
#>
#>
1 <R=1.000,thr=0.900,N= 12>, Top: 6( 1 )[ 1 : 6 Fa= 6 : 0.900 ]( 6 , 6 , 0 ),<|>Tot Used: 12 , Added: 6 , Zero Std: 0 , Max Cor: 0.894
#>
2 <R=0.894,thr=0.750,N= 97>, Top: 36( 14 )[ 1 : 36 Fa= 38 : 0.750 ]( 35 , 51 , 6 ),<|>Tot Used: 94 , Added: 51 , Zero Std: 0 , Max Cor: 0.963
#>
3 <R=0.963,thr=0.900,N= 16>, Top: 8( 1 )[ 1 : 8 Fa= 45 : 0.900 ]( 8 , 8 , 38 ),<|>Tot Used: 101 , Added: 8 , Zero Std: 0 , Max Cor: 0.872
#>
4 <R=0.872,thr=0.750,N= 11>, Top: 5( 1 )[ 1 : 5 Fa= 48 : 0.750 ]( 4 , 6 , 45 ),<|>Tot Used: 106 , Added: 6 , Zero Std: 0 , Max Cor: 0.902
#>
5 <R=0.902,thr=0.900,N= 2>, Top: 1( 1 )[ 1 : 1 Fa= 49 : 0.900 ]( 1 , 1 , 48 ),<|>Tot Used: 106 , Added: 1 , Zero Std: 0 , Max Cor: 0.834
#>
6 <R=0.834,thr=0.750,N= 2>, Top: 1( 1 )[ 1 : 1 Fa= 50 : 0.750 ]( 1 , 1 , 49 ),<|>Tot Used: 106 , Added: 1 , Zero Std: 0 , Max Cor: 0.749
#>
7 <R=0.749,thr=0.600,N= 122>, Top: 31( 19 )[ 1 : 31 Fa= 73 : 0.600 ]( 30 , 57 , 50 ),<|>Tot Used: 163 , Added: 57 , Zero Std: 0 , Max Cor: 0.962
#>
8 <R=0.962,thr=0.900,N= 2>, Top: 1( 1 )[ 1 : 1 Fa= 74 : 0.900 ]( 1 , 1 , 73 ),<|>Tot Used: 164 , Added: 1 , Zero Std: 0 , Max Cor: 0.871
#>
9 <R=0.871,thr=0.750,N= 12>, Top: 6( 1 )[ 1 : 6 Fa= 78 : 0.750 ]( 6 , 6 , 74 ),<|>Tot Used: 168 , Added: 6 , Zero Std: 0 , Max Cor: 0.872
#>
10 <R=0.872,thr=0.750,N= 12>, Top: 3( 1 )[ 1 : 3 Fa= 79 : 0.750 ]( 3 , 3 , 78 ),<|>Tot Used: 168 , Added: 3 , Zero Std: 0 , Max Cor: 0.746
#>
11 <R=0.746,thr=0.600,N= 47>, Top: 13( 2 )[ 1 : 13 Fa= 88 : 0.600 ]( 13 , 26 , 79 ),<|>Tot Used: 188 , Added: 26 , Zero Std: 0 , Max Cor: 0.928
#>
12 <R=0.928,thr=0.900,N= 2>, Top: 1( 1 )[ 1 : 1 Fa= 89 : 0.900 ]( 1 , 1 , 88 ),<|>Tot Used: 189 , Added: 1 , Zero Std: 0 , Max Cor: 0.866
#>
13 <R=0.866,thr=0.750,N= 4>, Top: 2( 1 )[ 1 : 2 Fa= 91 : 0.750 ]( 2 , 2 , 89 ),<|>Tot Used: 189 , Added: 2 , Zero Std: 0 , Max Cor: 0.747
#>
14 <R=0.747,thr=0.600,N= 20>, Top: 9( 1 )[ 1 : 9 Fa= 93 : 0.600 ]( 9 , 9 , 91 ),<|>Tot Used: 191 , Added: 9 , Zero Std: 0 , Max Cor: 0.926
#>
15 <R=0.926,thr=0.900,N= 2>, Top: 1( 1 )[ 1 : 1 Fa= 94 : 0.900 ]( 1 , 1 , 93 ),<|>Tot Used: 191 , Added: 1 , Zero Std: 0 , Max Cor: 0.817
#>
16 <R=0.817,thr=0.750,N= 2>, Top: 1( 1 )[ 1 : 1 Fa= 95 : 0.750 ]( 1 , 1 , 94 ),<|>Tot Used: 191 , Added: 1 , Zero Std: 0 , Max Cor: 0.707
#>
17 <R=0.707,thr=0.600,N= 2>, Top: 1( 1 )[ 1 : 1 Fa= 96 : 0.600 ]( 1 , 1 , 95 ),<|>Tot Used: 191 , Added: 1 , Zero Std: 0 , Max Cor: 0.690
#>
18 <R=0.690,thr=0.600,N= 2>, Top: 1( 1 )[ 1 : 1 Fa= 96 : 0.600 ]( 1 , 1 , 96 ),<|>Tot Used: 191 , Added: 1 , Zero Std: 0 , Max Cor: 0.667
#>
19 <R=0.667,thr=0.600,N= 2>, Top: 1( 1 )[ 1 : 1 Fa= 97 : 0.600 ]( 1 , 1 , 96 ),<|>Tot Used: 191 , Added: 1 , Zero Std: 0 , Max Cor: 0.599
#>
20 <R=0.599,thr=0.600,N= 0>
#>
[ 20 ], 0.5992682 Decor Dimension: 191 Nused: 191 . Cor to Base: 116 , ABase: 45 , Outcome Base: 0
#>
varlistc <- colnames(DEdataframe)[colnames(DEdataframe) != outcome]
pander::pander(sum(apply(dataframe[,varlist],2,var)))
1377
pander::pander(sum(apply(DEdataframe[,varlistc],2,var)))
416
pander::pander(entropy(discretize(unlist(dataframe[,varlist]), 256)))
1.01
pander::pander(entropy(discretize(unlist(DEdataframe[,varlistc]), 256)))
0.762
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
UPSTM <- attr(DEdataframe,"UPSTM")
gplots::heatmap.2(1.0*(abs(UPSTM)>0),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Decorrelation matrix",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Beta|>0",
xlab="Output Feature", ylab="Input Feature")
par(op)
}
if (!largeSet)
{
cormat <- cor(DEdataframe[,varlistc],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Correlation after IDeA",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
par(op)
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.5992682
if (nrow(dataframe) < 1000)
{
classes <- unique(dataframe[1:numsub,outcome])
raincolors <- rainbow(length(classes))
names(raincolors) <- classes
datasetframe.umap = umap(scale(dataframe[1:numsub,varlist]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: Original",t='n')
text(datasetframe.umap$layout,labels=dataframe[1:numsub,outcome],col=raincolors[dataframe[1:numsub,outcome]+1])
}
if (nrow(dataframe) < 1000)
{
datasetframe.umap = umap(scale(DEdataframe[1:numsub,varlistc]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: After IDeA",t='n')
text(datasetframe.umap$layout,labels=DEdataframe[1:numsub,outcome],col=raincolors[DEdataframe[1:numsub,outcome]+1])
}
univarRAW <- uniRankVar(varlist,
paste(outcome,"~1"),
outcome,
dataframe,
rankingTest="AUC")
100 : M_ST24SA 200 : D_ST49TA 300 : D_ST47CV
univarDe <- uniRankVar(varlistc,
paste(outcome,"~1"),
outcome,
DEdataframe,
rankingTest="AUC",
)
100 : M_ST24SA 200 : D_ST49TA 300 : La_D_ST47CV
univariate_columns <- c("caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC")
##top variables
topvar <- c(1:length(varlist)) <= TopVariables
tableRaw <- univarRAW$orderframe[topvar,univariate_columns]
pander::pander(tableRaw)
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| ADAS13 | 20.7611 | 6.16923 | 14.0091 | 5.78970 | 0.03549 | 0.788 |
| ADAS11 | 12.8635 | 4.56128 | 8.7155 | 3.84978 | 0.00264 | 0.761 |
| FAQ | 5.4631 | 4.90262 | 1.9266 | 2.98257 | 0.00000 | 0.756 |
| M_ST40CV | 0.1799 | 0.00875 | 0.1875 | 0.00763 | 0.28199 | 0.750 |
| M_ST29SV | 0.1253 | 0.00708 | 0.1321 | 0.00750 | 0.58088 | 0.745 |
| M_ST12SV | 0.0913 | 0.00535 | 0.0962 | 0.00550 | 0.50030 | 0.744 |
| Hippocampus | 0.1582 | 0.00886 | 0.1664 | 0.00945 | 0.44340 | 0.737 |
| RAVLT_immediate | 29.0205 | 7.69236 | 37.2798 | 10.92838 | 0.04406 | 0.728 |
| M_ST24CV | 0.0996 | 0.00800 | 0.1059 | 0.00706 | 0.04673 | 0.727 |
| M_ST31CV | 0.1910 | 0.00945 | 0.1986 | 0.00902 | 0.94566 | 0.717 |
topLAvar <- univarDe$orderframe$Name[str_detect(univarDe$orderframe$Name,"La_")]
topLAvar <- unique(c(univarDe$orderframe$Name[topvar],topLAvar[1:as.integer(TopVariables/2)]))
finalTable <- univarDe$orderframe[topLAvar,univariate_columns]
pander::pander(finalTable)
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| ADAS11 | 12.86352 | 4.561284 | 8.71553 | 3.849783 | 2.64e-03 | 0.761 |
| FAQ | 5.46311 | 4.902619 | 1.92661 | 2.982574 | 0.00e+00 | 0.756 |
| Hippocampus | 0.15823 | 0.008856 | 0.16644 | 0.009452 | 4.43e-01 | 0.737 |
| M_ST31TA | 0.01874 | 0.001851 | 0.02006 | 0.001711 | 2.69e-01 | 0.703 |
| M_ST36CV | 0.15904 | 0.006107 | 0.16276 | 0.006117 | 5.31e-01 | 0.674 |
| RAVLT_learning | 3.17623 | 2.396347 | 4.66514 | 2.556538 | 1.35e-04 | 0.673 |
| La_M_ST30SV | -0.00580 | 0.010722 | -0.01268 | 0.011405 | 9.50e-01 | 0.669 |
| M_ST39SA | 0.34781 | 0.021709 | 0.36106 | 0.023122 | 4.59e-02 | 0.669 |
| MMSE | 26.91803 | 1.764255 | 27.96101 | 1.752723 | 2.78e-15 | 0.668 |
| M_ST49CV | 0.17209 | 0.007592 | 0.17643 | 0.008080 | 9.10e-01 | 0.651 |
| La_M_ST11SV | 0.04202 | 0.000658 | 0.04239 | 0.000695 | 8.29e-01 | 0.650 |
| La_ADAS13 | 2.67226 | 2.070109 | 1.75325 | 1.988479 | 4.56e-01 | 0.631 |
| La_M_ST37SV | 0.00158 | 0.006665 | 0.00198 | 0.002163 | 3.04e-05 | 0.625 |
| La_M_ST51CV | 0.01531 | 0.001446 | 0.01589 | 0.001451 | 4.83e-01 | 0.614 |
dc <- getLatentCoefficients(DEdataframe)
fscores <- attr(DEdataframe,"fscore")
pander::pander(c(mean=mean(sapply(dc,length)),total=length(dc),fraction=length(dc)/(ncol(dataframe)-1)))
| mean | total | fraction |
|---|---|---|
| 2.49 | 126 | 0.385 |
theCharformulas <- attr(dc,"LatentCharFormulas")
finalTable <- rbind(finalTable,tableRaw[topvar[!(topvar %in% topLAvar)],univariate_columns])
orgnamez <- rownames(finalTable)
orgnamez <- str_remove_all(orgnamez,"La_")
finalTable$RAWAUC <- univarRAW$orderframe[orgnamez,"ROCAUC"]
finalTable$DecorFormula <- theCharformulas[rownames(finalTable)]
finalTable$fscores <- fscores[rownames(finalTable)]
Final_Columns <- c("DecorFormula","caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC","RAWAUC","fscores")
finalTable <- finalTable[order(-finalTable$ROCAUC),]
pander::pander(finalTable[,Final_Columns])
| DecorFormula | caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | RAWAUC | fscores | |
|---|---|---|---|---|---|---|---|---|---|
| ADAS13 | NA | 20.76107 | 6.169227 | 14.00911 | 5.79e+00 | 3.55e-02 | 0.788 | 0.788 | NA |
| ADAS11 | NA | 12.86352 | 4.561284 | 8.71553 | 3.85e+00 | 2.64e-03 | 0.761 | 0.761 | 2 |
| ADAS111 | NA | 12.86352 | 4.561284 | 8.71553 | 3.85e+00 | 2.64e-03 | 0.761 | NA | NA |
| FAQ | NA | 5.46311 | 4.902619 | 1.92661 | 2.98e+00 | 0.00e+00 | 0.756 | 0.756 | NA |
| FAQ1 | NA | 5.46311 | 4.902619 | 1.92661 | 2.98e+00 | 0.00e+00 | 0.756 | NA | NA |
| M_ST40CV | NA | 0.17986 | 0.008748 | 0.18753 | 7.63e-03 | 2.82e-01 | 0.750 | 0.750 | NA |
| M_ST29SV | NA | 0.12530 | 0.007075 | 0.13210 | 7.50e-03 | 5.81e-01 | 0.745 | 0.745 | NA |
| M_ST12SV | NA | 0.09133 | 0.005353 | 0.09622 | 5.50e-03 | 5.00e-01 | 0.744 | 0.744 | NA |
| Hippocampus | NA | 0.15823 | 0.008856 | 0.16644 | 9.45e-03 | 4.43e-01 | 0.737 | 0.737 | 6 |
| Hippocampus1 | NA | 0.15823 | 0.008856 | 0.16644 | 9.45e-03 | 4.43e-01 | 0.737 | NA | NA |
| RAVLT_immediate | NA | 29.02049 | 7.692361 | 37.27982 | 1.09e+01 | 4.41e-02 | 0.728 | 0.728 | NA |
| M_ST24CV | NA | 0.09963 | 0.008002 | 0.10594 | 7.06e-03 | 4.67e-02 | 0.727 | 0.727 | NA |
| M_ST31CV | NA | 0.19103 | 0.009453 | 0.19857 | 9.02e-03 | 9.46e-01 | 0.717 | 0.717 | NA |
| M_ST31TA | NA | 0.01874 | 0.001851 | 0.02006 | 1.71e-03 | 2.69e-01 | 0.703 | 0.703 | 39 |
| M_ST36CV | NA | 0.15904 | 0.006107 | 0.16276 | 6.12e-03 | 5.31e-01 | 0.674 | 0.674 | 5 |
| RAVLT_learning | NA | 3.17623 | 2.396347 | 4.66514 | 2.56e+00 | 1.35e-04 | 0.673 | 0.673 | NA |
| La_M_ST30SV | - (0.319)Ventricles + M_ST30SV | -0.00580 | 0.010722 | -0.01268 | 1.14e-02 | 9.50e-01 | 0.669 | 0.681 | -1 |
| M_ST39SA | NA | 0.34781 | 0.021709 | 0.36106 | 2.31e-02 | 4.59e-02 | 0.669 | 0.669 | NA |
| MMSE | NA | 26.91803 | 1.764255 | 27.96101 | 1.75e+00 | 2.78e-15 | 0.668 | 0.668 | NA |
| M_ST49CV | NA | 0.17209 | 0.007592 | 0.17643 | 8.08e-03 | 9.10e-01 | 0.651 | 0.651 | NA |
| La_M_ST11SV | + (1.57e-04)ICV + M_ST11SV | 0.04202 | 0.000658 | 0.04239 | 6.95e-04 | 8.29e-01 | 0.650 | 0.645 | -1 |
| La_ADAS13 | - (1.406)ADAS11 + ADAS13 | 2.67226 | 2.070109 | 1.75325 | 1.99e+00 | 4.56e-01 | 0.631 | 0.788 | -1 |
| La_M_ST37SV | - (0.774)Ventricles + M_ST37SV | 0.00158 | 0.006665 | 0.00198 | 2.16e-03 | 3.04e-05 | 0.625 | 0.601 | -1 |
| La_M_ST51CV | + (2.345)M_ST31TA - (3.273)M_ST51TA + (0.144)M_ST31SA - (0.201)M_ST51SA - (0.645)M_ST31CV + M_ST51CV | 0.01531 | 0.001446 | 0.01589 | 1.45e-03 | 4.83e-01 | 0.614 | 0.648 | -4 |
featuresnames <- colnames(dataframe)[colnames(dataframe) != outcome]
pc <- prcomp(dataframe[,iscontinous],center = TRUE,scale. = TRUE) #principal components
predPCA <- predict(pc,dataframe[,iscontinous])
PCAdataframe <- as.data.frame(cbind(predPCA,dataframe[,!iscontinous]))
colnames(PCAdataframe) <- c(colnames(predPCA),colnames(dataframe)[!iscontinous])
#plot(PCAdataframe[,colnames(PCAdataframe)!=outcome],col=dataframe[,outcome],cex=0.65,cex.lab=0.5,cex.axis=0.75,cex.sub=0.5,cex.main=0.75)
#pander::pander(pc$rotation)
PCACor <- cor(PCAdataframe[,colnames(PCAdataframe) != outcome])
gplots::heatmap.2(abs(PCACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "PCA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
EFAdataframe <- dataframeScaled
if (length(iscontinous) < 2000)
{
topred <- min(length(iscontinous),nrow(dataframeScaled),ncol(predPCA)/2)
if (topred < 2) topred <- 2
uls <- fa(dataframeScaled[,iscontinous],nfactors=topred,rotate="varimax",warnings=FALSE) # EFA analysis
predEFA <- predict(uls,dataframeScaled[,iscontinous])
EFAdataframe <- as.data.frame(cbind(predEFA,dataframeScaled[,!iscontinous]))
colnames(EFAdataframe) <- c(colnames(predEFA),colnames(dataframeScaled)[!iscontinous])
EFACor <- cor(EFAdataframe[,colnames(EFAdataframe) != outcome])
gplots::heatmap.2(abs(EFACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "EFA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
}
par(op)
par(xpd = TRUE)
dataframe[,outcome] <- factor(dataframe[,outcome])
rawmodel <- rpart(paste(outcome,"~."),dataframe,control=rpart.control(maxdepth=3))
pr <- predict(rawmodel,dataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(rawmodel,main="Raw",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(rawmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,dataframe[,outcome]==0))
}
pander::pander(table(dataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 330 | 106 |
| 1 | 36 | 208 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.791 | 0.759 | 0.821 |
| 3 | se | 0.852 | 0.802 | 0.894 |
| 4 | sp | 0.757 | 0.714 | 0.796 |
| 6 | diag.or | 17.987 | 11.866 | 27.267 |
par(op)
par(xpd = TRUE)
DEdataframe[,outcome] <- factor(DEdataframe[,outcome])
IDeAmodel <- rpart(paste(outcome,"~."),DEdataframe,control=rpart.control(maxdepth=3))
pr <- predict(IDeAmodel,DEdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(IDeAmodel,main="IDeA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(IDeAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,DEdataframe[,outcome]==0))
}
pander::pander(table(DEdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 371 | 65 |
| 1 | 89 | 155 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.774 | 0.740 | 0.804 |
| 3 | se | 0.635 | 0.571 | 0.696 |
| 4 | sp | 0.851 | 0.814 | 0.883 |
| 6 | diag.or | 9.940 | 6.862 | 14.401 |
par(op)
par(xpd = TRUE)
PCAdataframe[,outcome] <- factor(PCAdataframe[,outcome])
PCAmodel <- rpart(paste(outcome,"~."),PCAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(PCAmodel,PCAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(PCAmodel,main="PCA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(PCAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,PCAdataframe[,outcome]==0))
}
pander::pander(table(PCAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 401 | 35 |
| 1 | 122 | 122 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.769 | 0.736 | 0.800 |
| 3 | se | 0.500 | 0.436 | 0.564 |
| 4 | sp | 0.920 | 0.890 | 0.943 |
| 6 | diag.or | 11.457 | 7.476 | 17.559 |
par(op)
EFAdataframe[,outcome] <- factor(EFAdataframe[,outcome])
EFAmodel <- rpart(paste(outcome,"~."),EFAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(EFAmodel,EFAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(EFAmodel,main="EFA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(EFAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,EFAdataframe[,outcome]==0))
}
pander::pander(table(EFAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 383 | 53 |
| 1 | 91 | 153 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.788 | 0.756 | 0.818 |
| 3 | se | 0.627 | 0.563 | 0.688 |
| 4 | sp | 0.878 | 0.844 | 0.908 |
| 6 | diag.or | 12.150 | 8.250 | 17.893 |
par(op)